{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:36.266885Z",
     "start_time": "2025-02-04T06:55:30.359319Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:30:35.733311Z",
     "iopub.status.busy": "2025-02-04T08:30:35.732840Z",
     "iopub.status.idle": "2025-02-04T08:30:42.146993Z",
     "shell.execute_reply": "2025-02-04T08:30:42.145974Z",
     "shell.execute_reply.started": "2025-02-04T08:30:35.733280Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:45.587575Z",
     "start_time": "2025-02-04T06:55:36.267873Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:31:58.561872Z",
     "iopub.status.busy": "2025-02-04T08:31:58.561304Z",
     "iopub.status.idle": "2025-02-04T08:32:07.959997Z",
     "shell.execute_reply": "2025-02-04T08:32:07.959135Z",
     "shell.execute_reply.started": "2025-02-04T08:31:58.561832Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-02-04 16:31:58.825947: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1738657918.852237     435 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1738657918.860286     435 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2025-02-04 16:31:58.888075: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
      "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "#用karas有的数据集imdb，电影分类,分电影是积极的，还是消极的\n",
    "imdb = keras.datasets.imdb\n",
    "#载入数据使用下面两个参数\n",
    "vocab_size = 10000  #词典大小，仅保留训练数据中前10000个最经常出现的单词，低频单词被舍弃\n",
    "index_from = 3  #0,1,2,3空出来做别的事\n",
    "#前一万个词出现词频最高的会保留下来进行处理，后面的作为特殊字符处理，\n",
    "# 小于3的id都是特殊字符，下面代码有写\n",
    "# 需要注意的一点是取出来的词表还是从1开始的，需要做处理\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
    "    num_words = vocab_size, index_from = index_from)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:45.593748Z",
     "start_time": "2025-02-04T06:55:45.588570Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:32:12.245055Z",
     "iopub.status.busy": "2025-02-04T08:32:12.244243Z",
     "iopub.status.idle": "2025-02-04T08:32:12.250015Z",
     "shell.execute_reply": "2025-02-04T08:32:12.249030Z",
     "shell.execute_reply.started": "2025-02-04T08:32:12.245016Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train (25000,) (25000,)\n",
      "test (25000,) (25000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"train\", train_data.shape, train_labels.shape)\n",
    "print(\"test\", test_data.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:45.666135Z",
     "start_time": "2025-02-04T06:55:45.594736Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:32:14.819958Z",
     "iopub.status.busy": "2025-02-04T08:32:14.819441Z",
     "iopub.status.idle": "2025-02-04T08:32:16.511135Z",
     "shell.execute_reply": "2025-02-04T08:32:16.510344Z",
     "shell.execute_reply.started": "2025-02-04T08:32:14.819922Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
      "\u001b[1m1641221/1641221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1us/step\n",
      "88584\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "#载入词表，看下词表长度，词表就像英语字典\n",
    "word_index = imdb.get_word_index()\n",
    "print(len(word_index))\n",
    "print(type(word_index))\n",
    "#词表虽然有8万多，但是我们只载入了最高频的1万词！！！！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构造 word2idx 和 idx2word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:45.716059Z",
     "start_time": "2025-02-04T06:55:45.668263Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:32:19.535962Z",
     "iopub.status.busy": "2025-02-04T08:32:19.535444Z",
     "iopub.status.idle": "2025-02-04T08:32:19.589602Z",
     "shell.execute_reply": "2025-02-04T08:32:19.588814Z",
     "shell.execute_reply.started": "2025-02-04T08:32:19.535925Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "word2idx = {word: idx + 3 for word, idx in word_index.items()}\n",
    "word2idx.update({\n",
    "    \"[PAD]\": 0,     # 填充 token\n",
    "    \"[BOS]\": 1,     # begin of sentence\n",
    "    \"[UNK]\": 2,     # 未知 token\n",
    "    \"[EOS]\": 3,     # end of sentence\n",
    "})\n",
    "\n",
    "idx2word = {idx: word for word, idx in word2idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:46.718809Z",
     "start_time": "2025-02-04T06:55:45.717887Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:32:23.157282Z",
     "iopub.status.busy": "2025-02-04T08:32:23.156771Z",
     "iopub.status.idle": "2025-02-04T08:32:24.548035Z",
     "shell.execute_reply": "2025-02-04T08:32:24.547112Z",
     "shell.execute_reply.started": "2025-02-04T08:32:23.157247Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 选择 max_length\n",
    "length_collect = {}\n",
    "for text in train_data:\n",
    "    length = len(text)\n",
    "    length_collect[length] = length_collect.get(length, 0) + 1\n",
    "    \n",
    "MAX_LENGTH = 500\n",
    "plt.bar(length_collect.keys(), length_collect.values())\n",
    "plt.axvline(MAX_LENGTH, label=\"max length\", c=\"gray\", ls=\":\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:46.751022Z",
     "start_time": "2025-02-04T06:55:46.719795Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:32:53.841803Z",
     "iopub.status.busy": "2025-02-04T08:32:53.841273Z",
     "iopub.status.idle": "2025-02-04T08:32:53.881787Z",
     "shell.execute_reply": "2025-02-04T08:32:53.881074Z",
     "shell.execute_reply.started": "2025-02-04T08:32:53.841767Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw text\n",
      "['hello', 'world']\n",
      "['tokenize', 'text', 'datas', 'with', 'batch']\n",
      "['this', 'is', 'a', 'test']\n",
      "indices\n",
      "tensor([   0,    0,    0,    1, 4825,  182,    3])\n",
      "tensor([    1,     2,  3004,     2,    19, 19233,     3])\n",
      "tensor([   0,    1,   14,    9,    6, 2181,    3])\n",
      "decode text\n",
      "[PAD] [PAD] [PAD] [BOS] hello world [EOS]\n",
      "[BOS] [UNK] text [UNK] with batch [EOS]\n",
      "[PAD] [BOS] this is a test [EOS]\n"
     ]
    }
   ],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=500, pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "    \n",
    "    def encode(self, text_list, padding_first=False):\n",
    "        \"\"\"如果padding_first == True，则padding加载前面，否则加载后面\"\"\"\n",
    "        max_length = min(self.max_length, 2 + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            indices = [self.bos_idx] + [self.word2idx.get(word, self.unk_idx) for word in text[:max_length-2]] + [self.eos_idx] #直接切片取前max_length-2个单词，然后加上bos和eos\n",
    "            if padding_first:\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        return torch.tensor(indices_list)\n",
    "    \n",
    "    \n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":\n",
    "                    break\n",
    "                text.append(word)\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "    \n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "indices = tokenizer.encode(raw_text, padding_first=True)\n",
    "decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "print(\"raw text\")\n",
    "for raw in raw_text:\n",
    "    print(raw)\n",
    "print(\"indices\")\n",
    "for index in indices:\n",
    "    print(index)\n",
    "print(\"decode text\")\n",
    "for decode in decode_text:\n",
    "    print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:46.757622Z",
     "start_time": "2025-02-04T06:55:46.752017Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:32:55.755882Z",
     "iopub.status.busy": "2025-02-04T08:32:55.755361Z",
     "iopub.status.idle": "2025-02-04T08:32:55.762319Z",
     "shell.execute_reply": "2025-02-04T08:32:55.761393Z",
     "shell.execute_reply.started": "2025-02-04T08:32:55.755848Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"[BOS] this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert [UNK] is an amazing actor and now the same being director [UNK] father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for [UNK] and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also [UNK] to the two little boy's that played the [UNK] of norman and paul they were just brilliant children are often left out of the [UNK] list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\",\n",
       " \"[BOS] big hair big boobs bad music and a giant safety pin these are the words to best describe this terrible movie i love cheesy horror movies and i've seen hundreds but this had got to be on of the worst ever made the plot is paper thin and ridiculous the acting is an abomination the script is completely laughable the best is the end showdown with the cop and how he worked out who the killer is it's just so damn terribly written the clothes are sickening and funny in equal [UNK] the hair is big lots of boobs [UNK] men wear those cut [UNK] shirts that show off their [UNK] sickening that men actually wore them and the music is just [UNK] trash that plays over and over again in almost every scene there is trashy music boobs and [UNK] taking away bodies and the gym still doesn't close for [UNK] all joking aside this is a truly bad film whose only charm is to look back on the disaster that was the 80's and have a good old laugh at how bad everything was back then\"]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看训练集的数据\n",
    "tokenizer.decode(train_data[:2], remove_bos=False, remove_eos=False, remove_pad=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 数据集与 DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:48.842725Z",
     "start_time": "2025-02-04T06:55:46.758781Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:32:59.677852Z",
     "iopub.status.busy": "2025-02-04T08:32:59.677333Z",
     "iopub.status.idle": "2025-02-04T08:33:03.177334Z",
     "shell.execute_reply": "2025-02-04T08:33:03.176324Z",
     "shell.execute_reply.started": "2025-02-04T08:32:59.677813Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class IMDBDataset(Dataset):\n",
    "    def __init__(self, data, labels, remain_length=True):\n",
    "        if remain_length:\n",
    "            self.data = tokenizer.decode(data, remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "        else:\n",
    "            # 缩减一下数据\n",
    "            self.data = tokenizer.decode(data)\n",
    "        self.labels = labels\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        text = self.data[index]\n",
    "        label = self.labels[index]\n",
    "        return text, label\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "    \n",
    "    \n",
    "def collate_fct(batch):\n",
    "    text_list = [item[0].split() for item in batch]\n",
    "    label_list = [item[1] for item in batch]\n",
    "    # 这里使用 padding first\n",
    "    text_list = tokenizer.encode(text_list, padding_first=True).to(dtype=torch.int)\n",
    "    return text_list, torch.tensor(label_list).reshape(-1, 1).to(dtype=torch.float)\n",
    "\n",
    "\n",
    "# 用RNN，缩短序列长度\n",
    "train_ds = IMDBDataset(train_data, train_labels, remain_length=False)\n",
    "test_ds = IMDBDataset(test_data, test_labels, remain_length=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:48.847441Z",
     "start_time": "2025-02-04T06:55:48.843712Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:33:03.179087Z",
     "iopub.status.busy": "2025-02-04T08:33:03.178672Z",
     "iopub.status.idle": "2025-02-04T08:33:03.183622Z",
     "shell.execute_reply": "2025-02-04T08:33:03.182825Z",
     "shell.execute_reply.started": "2025-02-04T08:33:03.179057Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True, collate_fn=collate_fct)\n",
    "test_dl = DataLoader(test_ds, batch_size=batch_size, shuffle=False, collate_fn=collate_fct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:55:48.892407Z",
     "start_time": "2025-02-04T06:55:48.848439Z"
    },
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2025-02-04T08:33:03.184847Z",
     "iopub.status.busy": "2025-02-04T08:33:03.184522Z",
     "iopub.status.idle": "2025-02-04T08:33:03.230519Z",
     "shell.execute_reply": "2025-02-04T08:33:03.229863Z",
     "shell.execute_reply.started": "2025-02-04T08:33:03.184821Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 500]) torch.Size([128, 1])\n"
     ]
    }
   ],
   "source": [
    "for text, label in train_dl:\n",
    "    print(text.shape, label.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:06:40.565693Z",
     "start_time": "2025-02-04T08:06:40.558584Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:33:11.566291Z",
     "iopub.status.busy": "2025-02-04T08:33:11.565719Z",
     "iopub.status.idle": "2025-02-04T08:33:11.579383Z",
     "shell.execute_reply": "2025-02-04T08:33:11.578567Z",
     "shell.execute_reply.started": "2025-02-04T08:33:11.566250Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================== 一层单向 RNN ===================================\n",
      "            embeding.weight             paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "              layer.weight              paramerters num: 4096\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "class RNN(nn.Module):\n",
    "    def __init__(self, embedding_dim=16, hidden_dim=64, vocab_size=vocab_size, num_layers=1, bidirectional=False):\n",
    "        super(RNN, self).__init__()\n",
    "        self.embeding = nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.rnn = nn.RNN(embedding_dim, hidden_dim, num_layers=num_layers, batch_first=True, bidirectional=bidirectional)\n",
    "        self.layer = nn.Linear(hidden_dim * (2 if bidirectional else 1), hidden_dim)  # bidirectional双向输出，默认False\n",
    "        self.fc = nn.Linear(hidden_dim, 1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # [bs, seq length]->[bs, seq length, embedding_dim]\n",
    "        x = self.embeding(x)\n",
    "        # print(f'x.size()={x.size()}')\n",
    "        # [bs, seq length, embedding_dim] -> [bs,  seq length, hidden_dim],[ 1, bs,  hidden_dim]\n",
    "        seq_output, final_hidden = self.rnn(x)\n",
    "        # print(f'seq_output.size()={seq_output.size()}')\n",
    "        # print(f'final_hidden.size()={final_hidden.size()}')\n",
    "        # [ bs,  hidden_dim]\n",
    "        x = seq_output[:, -1, :]  # 取最后一个时间步的输出（原先有seq length个输出），这里的x=final_hidden\n",
    "        # print(f'x.size()={x.size()}')\n",
    "        # 取最后一个时间步的输出 (这也是为什么要设置padding_first=True的原因)\n",
    "        x = self.layer(x)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "print(\"{:=^80}\".format(\" 一层单向 RNN \"))       \n",
    "for key, value in RNN().named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")  # ih是input*hidden=16*64，hh是W=64*64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:05:35.275854Z",
     "start_time": "2025-02-04T08:05:35.239753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x.size()=torch.Size([2, 500, 16])\n",
      "seq_output.size()=torch.Size([2, 500, 64])\n",
      "final_hidden.size()=torch.Size([1, 2, 64])\n",
      "x.size()=torch.Size([2, 64])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # 前向计算，看输出的shape\n",
    "model = RNN()\n",
    "# 形状为(1, 500),大小为0-10000\n",
    "sample_inputs = torch.randint(0, vocab_size, (2, 500))  # vocab_size是词表大小，这里是10000\n",
    "model(sample_inputs).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T06:56:20.148334Z",
     "start_time": "2025-02-04T06:56:20.140310Z"
    },
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:33:22.596678Z",
     "iopub.status.busy": "2025-02-04T08:33:22.596162Z",
     "iopub.status.idle": "2025-02-04T08:33:22.604464Z",
     "shell.execute_reply": "2025-02-04T08:33:22.603633Z",
     "shell.execute_reply.started": "2025-02-04T08:33:22.596629Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================== 一层双向 RNN ===================================\n",
      "            embeding.weight             paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "        rnn.weight_ih_l0_reverse        paramerters num: 1024\n",
      "        rnn.weight_hh_l0_reverse        paramerters num: 4096\n",
      "         rnn.bias_ih_l0_reverse         paramerters num: 64\n",
      "         rnn.bias_hh_l0_reverse         paramerters num: 64\n",
      "              layer.weight              paramerters num: 8192\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"{:=^80}\".format(\" 一层双向 RNN \"))\n",
    "for key, value in RNN(bidirectional=True).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:45:51.199597Z",
     "iopub.status.busy": "2025-02-04T08:45:51.199098Z",
     "iopub.status.idle": "2025-02-04T08:45:51.207509Z",
     "shell.execute_reply": "2025-02-04T08:45:51.206781Z",
     "shell.execute_reply.started": "2025-02-04T08:45:51.199564Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================== 双层单向 RNN ===================================\n",
      "            embeding.weight             paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "            rnn.weight_ih_l1            paramerters num: 4096\n",
      "            rnn.weight_hh_l1            paramerters num: 4096\n",
      "             rnn.bias_ih_l1             paramerters num: 64\n",
      "             rnn.bias_hh_l1             paramerters num: 64\n",
      "              layer.weight              paramerters num: 4096\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"{:=^80}\".format(\" 双层单向 RNN \"))       \n",
    "for key, value in RNN(num_layers=2).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\") # 第二层的输入已经是64*64而不是最初的16*64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:45:48.871270Z",
     "iopub.status.busy": "2025-02-04T08:45:48.870767Z",
     "iopub.status.idle": "2025-02-04T08:45:48.879682Z",
     "shell.execute_reply": "2025-02-04T08:45:48.878917Z",
     "shell.execute_reply.started": "2025-02-04T08:45:48.871238Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================== 双层双向 RNN ===================================\n",
      "            embeding.weight             paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "        rnn.weight_ih_l0_reverse        paramerters num: 1024\n",
      "        rnn.weight_hh_l0_reverse        paramerters num: 4096\n",
      "         rnn.bias_ih_l0_reverse         paramerters num: 64\n",
      "         rnn.bias_hh_l0_reverse         paramerters num: 64\n",
      "            rnn.weight_ih_l1            paramerters num: 8192\n",
      "            rnn.weight_hh_l1            paramerters num: 4096\n",
      "             rnn.bias_ih_l1             paramerters num: 64\n",
      "             rnn.bias_hh_l1             paramerters num: 64\n",
      "        rnn.weight_ih_l1_reverse        paramerters num: 8192\n",
      "        rnn.weight_hh_l1_reverse        paramerters num: 4096\n",
      "         rnn.bias_ih_l1_reverse         paramerters num: 64\n",
      "         rnn.bias_hh_l1_reverse         paramerters num: 64\n",
      "              layer.weight              paramerters num: 8192\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"{:=^80}\".format(\" 双层双向 RNN \"))       \n",
    "for key, value in RNN(num_layers=2, bidirectional=True).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\") # 第二层的输入已经是64*64而不是最初的16*64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### evalueting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:08:24.134876Z",
     "start_time": "2025-02-04T08:08:23.744003Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:37:59.247697Z",
     "iopub.status.busy": "2025-02-04T08:37:59.247184Z",
     "iopub.status.idle": "2025-02-04T08:37:59.867546Z",
     "shell.execute_reply": "2025-02-04T08:37:59.866754Z",
     "shell.execute_reply.started": "2025-02-04T08:37:59.247663Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    pred_list = []\n",
    "    label_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        # 二分类\n",
    "        preds = logits > 0\n",
    "        pred_list.extend(preds.cpu().numpy().tolist())\n",
    "        label_list.extend(labels.cpu().numpy().tolist())\n",
    "        \n",
    "    acc = accuracy_score(label_list, pred_list)\n",
    "    return np.mean(loss_list), acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### TensorBoard 可视化\n",
    "\n",
    "\n",
    "训练过程中可以使用如下命令启动tensorboard服务。\n",
    "\n",
    "```shell\n",
    "tensorboard \\\n",
    "    --logdir=runs \\     # log 存放路径\n",
    "    --host 0.0.0.0 \\    # ip\n",
    "    --port 8848         # 端口\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:08:31.193112Z",
     "start_time": "2025-02-04T08:08:31.037078Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:38:02.346892Z",
     "iopub.status.busy": "2025-02-04T08:38:02.345857Z",
     "iopub.status.idle": "2025-02-04T08:38:02.512801Z",
     "shell.execute_reply": "2025-02-04T08:38:02.512033Z",
     "shell.execute_reply.started": "2025-02-04T08:38:02.346856Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "\n",
    "class TensorBoardCallback:\n",
    "    def __init__(self, log_dir, flush_secs=10):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            log_dir (str): dir to write log.\n",
    "            flush_secs (int, optional): write to dsk each flush_secs seconds. Defaults to 10.\n",
    "        \"\"\"\n",
    "        self.writer = SummaryWriter(log_dir=log_dir, flush_secs=flush_secs)\n",
    "\n",
    "    def draw_model(self, model, input_shape):\n",
    "        self.writer.add_graph(model, input_to_model=torch.randn(input_shape))\n",
    "        \n",
    "    def add_loss_scalars(self, step, loss, val_loss):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/loss\", \n",
    "            tag_scalar_dict={\"loss\": loss, \"val_loss\": val_loss},\n",
    "            global_step=step,\n",
    "            )\n",
    "        \n",
    "    def add_acc_scalars(self, step, acc, val_acc):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/accuracy\",\n",
    "            tag_scalar_dict={\"accuracy\": acc, \"val_accuracy\": val_acc},\n",
    "            global_step=step,\n",
    "        )\n",
    "        \n",
    "    def add_lr_scalars(self, step, learning_rate):\n",
    "        self.writer.add_scalars(\n",
    "            main_tag=\"training/learning_rate\",\n",
    "            tag_scalar_dict={\"learning_rate\": learning_rate},\n",
    "            global_step=step,\n",
    "            \n",
    "        )\n",
    "    \n",
    "    def __call__(self, step, **kwargs):\n",
    "        # add loss\n",
    "        loss = kwargs.pop(\"loss\", None)\n",
    "        val_loss = kwargs.pop(\"val_loss\", None)\n",
    "        if loss is not None and val_loss is not None:\n",
    "            self.add_loss_scalars(step, loss, val_loss)\n",
    "        # add acc\n",
    "        acc = kwargs.pop(\"acc\", None)\n",
    "        val_acc = kwargs.pop(\"val_acc\", None)\n",
    "        if acc is not None and val_acc is not None:\n",
    "            self.add_acc_scalars(step, acc, val_acc)\n",
    "        # add lr\n",
    "        learning_rate = kwargs.pop(\"lr\", None)\n",
    "        if learning_rate is not None:\n",
    "            self.add_lr_scalars(step, learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Save Best\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:08:35.931506Z",
     "start_time": "2025-02-04T08:08:35.927505Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:38:05.635812Z",
     "iopub.status.busy": "2025-02-04T08:38:05.635289Z",
     "iopub.status.idle": "2025-02-04T08:38:05.642753Z",
     "shell.execute_reply": "2025-02-04T08:38:05.641978Z",
     "shell.execute_reply.started": "2025-02-04T08:38:05.635772Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        \"\"\"\n",
    "        Save checkpoints each save_epoch epoch. \n",
    "        We save checkpoint by epoch in this implementation.\n",
    "        Usually, training scripts with pytorch evaluating model and save checkpoint by step.\n",
    "\n",
    "        Args:\n",
    "            save_dir (str): dir to save checkpoint\n",
    "            save_epoch (int, optional): the frequency to save checkpoint. Defaults to 1.\n",
    "            save_best_only (bool, optional): If True, only save the best model or save each model at every epoch.\n",
    "        \"\"\"\n",
    "        self.save_dir = save_dir\n",
    "        self.save_step = save_step\n",
    "        self.save_best_only = save_best_only\n",
    "        self.best_metrics = -1\n",
    "        \n",
    "        # mkdir\n",
    "        if not os.path.exists(self.save_dir):\n",
    "            os.mkdir(self.save_dir)\n",
    "        \n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return\n",
    "        \n",
    "        if self.save_best_only:\n",
    "            assert metric is not None\n",
    "            if metric >= self.best_metrics:\n",
    "                # save checkpoints\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"best.ckpt\"))\n",
    "                # update best metrics\n",
    "                self.best_metrics = metric\n",
    "        else:\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Early Stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:08:39.658335Z",
     "start_time": "2025-02-04T08:08:39.653586Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:38:09.761613Z",
     "iopub.status.busy": "2025-02-04T08:38:09.761122Z",
     "iopub.status.idle": "2025-02-04T08:38:09.767451Z",
     "shell.execute_reply": "2025-02-04T08:38:09.766687Z",
     "shell.execute_reply.started": "2025-02-04T08:38:09.761581Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:19:43.663221Z",
     "start_time": "2025-02-04T08:09:55.718451Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:38:40.296312Z",
     "iopub.status.busy": "2025-02-04T08:38:40.295537Z",
     "iopub.status.idle": "2025-02-04T08:41:50.232230Z",
     "shell.execute_reply": "2025-02-04T08:41:50.231446Z",
     "shell.execute_reply.started": "2025-02-04T08:38:40.296278Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3920/3920 [03:07<00:00, 20.90it/s, epoch=19]\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                preds = logits > 0\n",
    "            \n",
    "                acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())    \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"acc\": acc, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss, val_acc = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"acc\": val_acc, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "                    \n",
    "                    # 1. 使用 tensorboard 可视化\n",
    "                    if tensorboard_callback is not None:\n",
    "                        tensorboard_callback(\n",
    "                            global_step, \n",
    "                            loss=loss, val_loss=val_loss,\n",
    "                            acc=acc, val_acc=val_acc,\n",
    "                            lr=optimizer.param_groups[0][\"lr\"],\n",
    "                            )\n",
    "                \n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), metric=val_acc)\n",
    "\n",
    "                    # 3. 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(val_acc)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n",
    "        \n",
    "\n",
    "epoch = 20\n",
    "\n",
    "# model = RNN()\n",
    "model = RNN(bidirectional=True)\n",
    "# model = RNN(num_layers=2)\n",
    "# model = RNN(bidirectional=True,num_layers=2)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失 (但是二分类)\n",
    "loss_fct = F.binary_cross_entropy_with_logits\n",
    "# 2. 定义优化器 采用 adam\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 1. tensorboard 可视化\n",
    "if not os.path.exists(\"runs\"):\n",
    "    os.mkdir(\"runs\")\n",
    "tensorboard_callback = TensorBoardCallback(\"runs/imdb-rnn\")\n",
    "# tensorboard_callback.draw_model(model, [1, MAX_LENGTH])\n",
    "# 2. save best\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(\"checkpoints/imdb-rnn\", save_step=len(train_dl), save_best_only=True)\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=10)\n",
    "\n",
    "model = model.to(device)\n",
    "record = training(\n",
    "    model, \n",
    "    train_dl, \n",
    "    test_dl, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=tensorboard_callback,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_dl)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:22:45.558576Z",
     "start_time": "2025-02-04T08:22:45.403953Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:42:56.704358Z",
     "iopub.status.busy": "2025-02-04T08:42:56.703523Z",
     "iopub.status.idle": "2025-02-04T08:42:56.982499Z",
     "shell.execute_reply": "2025-02-04T08:42:56.981756Z",
     "shell.execute_reply.started": "2025-02-04T08:42:56.704322Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    fig_num = len(train_df.columns)\n",
    "    fig, axs = plt.subplots(1, fig_num, figsize=(5 * fig_num, 5))\n",
    "    for idx, item in enumerate(train_df.columns):    \n",
    "        axs[idx].plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        axs[idx].plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        axs[idx].grid()\n",
    "        axs[idx].legend()\n",
    "        # axs[idx].set_xticks(range(0, train_df.index[-1], 5000))\n",
    "        # axs[idx].set_xticklabels(map(lambda x: f\"{int(x/1000)}k\", range(0, train_df.index[-1], 5000)))\n",
    "        axs[idx].set_xlabel(\"step\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curves(record, sample_step=10)  #横坐标是 steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-04T08:23:08.492994Z",
     "start_time": "2025-02-04T08:23:01.503814Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:43:01.052200Z",
     "iopub.status.busy": "2025-02-04T08:43:01.051687Z",
     "iopub.status.idle": "2025-02-04T08:43:05.278613Z",
     "shell.execute_reply": "2025-02-04T08:43:05.277696Z",
     "shell.execute_reply.started": "2025-02-04T08:43:01.052165Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.5125\n",
      "accuracy: 0.7597\n"
     ]
    }
   ],
   "source": [
    "# dataload for evaluating\n",
    "\n",
    "# load checkpoints\n",
    "model.load_state_dict(torch.load(\"checkpoints/imdb-rnn/best.ckpt\",weights_only=True, map_location=\"cpu\"))\n",
    "\n",
    "model.eval()\n",
    "loss, acc = evaluating(model, test_dl, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\\naccuracy: {acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T08:52:52.604450Z",
     "iopub.status.busy": "2025-02-04T08:52:52.604075Z",
     "iopub.status.idle": "2025-02-04T08:56:05.830467Z",
     "shell.execute_reply": "2025-02-04T08:56:05.829687Z",
     "shell.execute_reply.started": "2025-02-04T08:52:52.604418Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3920/3920 [03:08<00:00, 20.77it/s, epoch=19]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.5179\n",
      "accuracy: 0.7646\n"
     ]
    }
   ],
   "source": [
    "# 双层单向\n",
    "model = RNN(num_layers=2)\n",
    "model = model.to(device)\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "early_stop_callback = EarlyStopCallback(patience=10)\n",
    "\n",
    "record = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    test_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    tensorboard_callback=tensorboard_callback,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_dl)\n",
    "    )\n",
    "\n",
    "plot_learning_curves(record, sample_step=10)  #横坐标是 steps\n",
    "\n",
    "model.load_state_dict(torch.load(\"checkpoints/imdb-rnn/best.ckpt\",weights_only=True, map_location=\"cpu\"))\n",
    "\n",
    "model.eval()\n",
    "loss, acc = evaluating(model, test_dl, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\\naccuracy: {acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-04T09:06:58.202441Z",
     "iopub.status.busy": "2025-02-04T09:06:58.201935Z",
     "iopub.status.idle": "2025-02-04T09:10:24.359798Z",
     "shell.execute_reply": "2025-02-04T09:10:24.358870Z",
     "shell.execute_reply.started": "2025-02-04T09:06:58.202406Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3920/3920 [03:20<00:00, 19.51it/s, epoch=19]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.4849\n",
      "accuracy: 0.7845\n"
     ]
    }
   ],
   "source": [
    "# 双层双向\n",
    "model = RNN(bidirectional=True,num_layers=2)\n",
    "model = model.to(device)\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "early_stop_callback = EarlyStopCallback(patience=10)\n",
    "\n",
    "record = training(\n",
    "    model, \n",
    "    train_dl, \n",
    "    test_dl, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=tensorboard_callback,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_dl)\n",
    "    )\n",
    "plot_learning_curves(record, sample_step=10)  #横坐标是 steps\n",
    "model.load_state_dict(torch.load(\"checkpoints/imdb-rnn/best.ckpt\",weights_only=True, map_location=\"cpu\"))\n",
    "\n",
    "model.eval()\n",
    "loss, acc = evaluating(model, test_dl, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\\naccuracy: {acc:.4f}\")"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [],
   "dockerImageVersionId": 30616,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
